In collaboration with AI21, Microsoft is excited to announce AI21’s Jamba-Instruct foundation chat completion model available now as a serverless API within Azure AI’s Models-as-a-Service. This marks a new collaboration between AI21 and Microsoft, and the first time Jamba-Instruct is widely available through a cloud partner. 

“We are thrilled to collaborate with Microsoft to introduce Jamba-Instruct to Microsoft Azure customers. This collaboration underscores our shared commitment to empowering enterprises with innovative AI solutions,” said Pankaj Dugar, SVP, GM North America of AI21. “Microsoft’s dedication to putting customers first and making cutting-edge technology accessible aligns perfectly with our mission at AI21. With Jamba-Instruct available on Azure AI, customers now have access to a transformative tool that will revolutionize how they approach language processing and drive unprecedented levels of efficiency and insight.” 

Azure AI provides a diverse array of advanced and user-friendly models, enabling customers to select the model that best fits their use case. Over the past eight months, we’ve expanded our model catalog through our partnerships with leading generative AI model providers to release their models and with Microsoft Research for releasing Phi-3. Azure AI model catalog lets developers select from over 1,600+ foundational models, including LLMs, SLMs, chat completion, and multimodal models from industry leaders like AI21, Cohere, Databricks, Deci AI, Hugging Face, Microsoft Research, Mistral AI, NVIDIA, OpenAI, and Stability AI. This extensive selection ensures that Azure customers can find the most suitable model for their unique use case.

The Jamba-Instruct model 

In offering Jamba-Instruct as part of the Models as a Service (MaaS) offering, both AI21 and Microsoft affirm their commitment to equipping enterprise developers with increased choice in models to build and scale generative AI applications through popular LLM developer tools like Azure AI prompt flow, Azure AI Common API Inference, LangChain, and AI21’s Azure client. 

Based on information from AI21, Jamba-Instruct is their most recent and powerful model to date. With the production-grade Mamba-based model, Jamba-Instruct leverages its hybrid architecture to achieve great performance, quality, and cost efficiency. In parallel, Azure AI offers a safe and compliant platform for setting up and running AI solutions, and a wide range of tools and services for creating, refining, and testing AI models. Additionally, Azure AI Content Safety offers filters that are enabled to screen for harmful content generated by the model, helping developers build safe and trustworthy applications.  

Features of Jamba-Instruct 

As outlined by AI21, Jamba-Instruct stands out for the following: 

Unprecedented context window length: Offering a 70K context window, Jamba-Instruct is built to effectively handle long context instances. From streamlining document comprehension to enabling more robust and sophisticated RAG Engine mechanisms, Jamba-Instruct’s context window opens new opportunities for powerful GenAI workflows. 

Superior performance on long context use cases: According to AI21, Jamba-Instruct outperforms across long context use case benchmarks, including QA (question answering) on earnings call transcripts and extracting key insights from lengthy legal documents. To view information on results for long-context QA benchmarks, on AI21’s Jamba base model, check out the whitepaper. 

Cost-efficient processing: Due to its unique hybrid architecture, Jamba-Instruct handles lengthy context on a smaller cloud footprint, according to AI21. 

Value for cost: According to AI21, Jamba-Instruct is highly competitive in common quality benchmarks, and its pricing makes it an intuitive choice for enterprises looking to build and scale GenAI applications while optimizing for cost. Simply put, Jamba-Instruct delivers high value for its cost among its size class. 

Accelerating time to production  

By utilizing the instruction-tuned Jamba-Instruct on Azure’s platform, organizations can harness the full potential of AI while having safe, reliable, and secure use. As an instruction-tuned model, Jamba-Instruct comes with built-in safety instructions, chat capabilities, and complex command comprehension needed to make it ready for immediate use by enterprises.

Developers using Jamba-Instruct can work seamlessly with tools in Azure AI Studio, such as Azure AI Content Safety, Azure AI Search, and prompt flow to enhance effective AI practices. Here are some main advantages that highlight the smooth integration and strong support system provided by Jamba-Instruct with Azure, Azure AI and Models as a Service: 

Enhanced Security and Compliance: Azure places a strong emphasis on data privacy and security, adopting Microsoft’s comprehensive security protocols to protect customer data. With Jamba-Instruct on Azure AI Studio, enterprises can operate confidently, knowing their data remains within the secure bounds of the Azure cloud, thereby enhancing privacy and operational efficiency.   

Content Safety Integration: Customers can integrate Jamba-Instruct models with content safety features available through Azure AI Content Safety, enabling additional responsible AI practices. This integration facilitates the development of safer AI applications, ensuring content generated or processed is monitored for compliance and ethical standards.  

Simplified Assessment of LLM flows: Azure AI’s prompt flow allows evaluation flows, which help developers to measure how well the outputs of LLMs match the given standards and goals by computing metrics. This feature is useful for workflows created with Jamba-Instruct; it enables a comprehensive assessment using metrics such as groundedness, which gauges the pertinence and accuracy of the model’s responses based on the input sources when using a retrieval augmented generation (RAG) pattern. 

Client integration: You can use the API and key with various clients. Use the provided API in Large Language Model (LLM) tools such as prompt flow, OpenAI, LangChain, LiteLLM, CLI with curl and Python web requests. Deeper integrations and further capabilities coming soon. 

Simplified Deployment and Inference: By deploying AI21 models through MaaS with pay-as-you-go inference APIs, developers can take advantage of the power of Jamba-Instruct without managing underlying infrastructure in their Azure environment. You can view the pricing on Azure Marketplace for Jamba-Instruct based on input and output token consumption. 

These features demonstrate Azure’s commitment to offering an environment where organizations can harness the full potential of AI technologies like Jamba-Instruct efficiently and responsibly, driving innovation while maintaining high standards of security and compliance. 

With Azure AI’s commitment to data security, privacy, and pay-as-you-go inference APIs, developers can focus on building, knowing that their enterprise data remains secure without needing to dedicate additional time to maintaining complex infrastructure. 

Getting started with Jamba-Instruct on Azure  

We invite you to innovate using Jamba-Instruct on Azure AI Studio. There are a wide variety of use cases that companies are building including automated term sheet generators, product description generators, and virtual health assistants. You can view various other use cases here.  

To start building, enter the Azure AI Studio model catalog and utilize the Jamba-Instruct model. To view documentation on getting started, visit this link. Deploying Jamba-Instruct takes a couple of minutes by following these clear steps:  

Familiarize Yourself: If you’re new to Azure AI Studio, start by reviewing this documentation to understand the basics and set up your first project.  
Access the Model Catalog: Open the model catalog in AI Studio.  
Find the Model: Use the filter to select the AI21 collection or click the “View models” button on the MaaS announcement card.  
Select the Model: Open the AI21-Jamba-Instruct model from the list.  
Deploy the Model: Click on ‘Deploy’ and choose the Pay-as-you-go (PAYG) deployment option.  
Subscribe and Access: Subscribe to the offer to gain access to the model (usage charges apply), then proceed to deploy it.  
Explore the Playground: After deployment, you will automatically be redirected to the Playground. Here, you can explore the model’s capabilities.  
Customize Settings: Adjust the context or inference parameters to fine-tune the model’s predictions to your needs.  
Access Programmatically: Click on the “View code” button to obtain the API, keys, and a code snippet. This enables you to access and integrate the model programmatically.  
Integrate with Tools: Use the provided API in Large Language Model (LLM) tools such as prompt flow, Semantic Kernel, LangChain, or any other tools that support REST API with key-based authentication for making inferences.  

FAQs   

What does it cost to use the Jamba-Instruct model on Azure?  

You are billed based on the number of prompt and completions tokens. You can review the pricing in the Marketplace offer details tab when deploying the model. You can also find the pricing on the Azure Marketplace.  
Paygo-inference-input tokens are 1k for 0.007, and paygo-inference-output-tokens are 1k for 0.0005.  

Do I need GPU capacity in my Azure subscription to use Jamba-Instruct?  

No, you do not need GPU capacity. The Jamba-Instruct is offered as an API through Models as a Service.   

Is Jamba-Instruct available in Azure Machine Learning Studio?  

Yes, Jamba-Instruct is available in the model catalog in both Azure AI Studio and Azure Machine Learning Studio.  

Jamba-Instruct is listed on the Azure Marketplace. Can I purchase and use Jamba-Instruct directly from Azure Marketplace?  

Azure Marketplace is our foundation for commercial transactions for models built on or built for Azure. The Azure Marketplace enables the purchasing and billing of Jamba-Instruct. However, model discoverability occurs in both Azure Marketplace and the Azure AI model catalog. Meaning you can search and find Jamba-Instruct in both the Azure Marketplace and Azure AI model catalog. 

If you search for Jamba-Instruct in Azure Marketplace, you can subscribe to the offer before being redirected to the Azure AI model catalog in Azure AI Studio where you can complete subscribing and can deploy the model. 
If you search for Jamba-Instruct in the Azure AI model catalog, you can subscribe and deploy the model from the Azure AI model catalog without starting from the Azure Marketplace. The Azure Marketplace still tracks the underlying commerce flow. 

Given that Jamba-Instruct is billed through the Azure Marketplace, does it retire my Azure consumption commitment (aka MACC)?  

Yes, Jamba-Instruct is an “Azure benefit eligible” Marketplace offer, which indicates MACC eligibility. Learn more about MACC here: https://learn.microsoft.com/en-us/marketplace/azure-consumption-commitment-benefit   

Is my inference data shared with AI21? What about safety, privacy, and data processing?

No, Microsoft does not share the content from prompts or outputs with AI21. Learn more about data use through model catalog here: Data, privacy, and security for use of models through the Model Catalog in Azure AI Studio  

Are there rate limits for the Jamba-Instruct model on Azure?  

Yes, there are rate limits for the Jamba-Instruct model on Azure. Each deployment has a rate limit of 400 tokens per minute and 1,000 API requests per minute. Contact Azure customer support if you have additional questions. 

Is the Jamba-Instruct model region specific?  

Jamba-Instruct model API endpoints can be created in AI Studio projects to Azure Machine Learning workspaces in EastUS2 and Sweden Central. If you want to use the model in prompt flow in project or workspaces in other regions, you can use the API and key as a connection to prompt flow manually. Essentially, you can use the API from any Azure region once you create it in EastUS2 or Sweden Central.  

Can I fine-tune the Jamba-Instruct model on Azure?  

You cannot currently fine-tune the model through Azure AI Studio. 

Can I use MaaS models in any Azure subscription types? 

Customers can use MaaS models in all Azure subsection types with a valid payment method, except for the CSP (Cloud Solution Provider) program. Free or trial Azure subscriptions are not supported. 

​Microsoft Tech Community – Latest Blogs –Read More 

As spring turns to summer, and sprouts appear in my garden, I think about all the preparation I’ve made for their environment—turning the soil, setting up the watering system, adding peat moss—and know that the yield will be greater and the harvest better. Such is the case with Microsoft Intune. As we continue to enhance the capabilities, each one an investment, the cumulative result is a richer and more robust management experience. Below, we highlight some of the newest features.

New troubleshooting tool for mobile devices

Part of diagnosing an issue is not only defining what is wrong but also what is not wrong. Our customers asked for a simple way to temporarily remove apps and configurations from a device managed in Microsoft Intune as part of the troubleshooting process. The result is a feature we call Remove apps and configuration (RAC). Before RAC, removing settings involved excluding devices from policy assignments or removing users from groups, and then waiting for devices to check in. After diagnosing the device, those assignments and group memberships would need to be restored one by one. Now, RAC affords a set of useful troubleshooting steps:

Real-time monitoring of which policies and apps are removed/restored
Selective restore of individual apps and policies
Temporary removal of apps and policies with an automated restore in 8 to 24 hours
Policy assignments and group membership remain unchanged

This initial release will be distributed in early July. It will support iOS/iPadOS and Android corporate-owned devices, and it will be available to GCC, GCC High, and DoD environments on release. For more information on this tool, follow the update on the Microsoft 365 roadmap.

Windows enrollment attestation preview is here

Last month we talked about device attestation capabilities coming to Intune. This month, the public preview of Windows enrollment attestation is starting to roll out with the new reporting and Device Attest action.

This feature builds on attestation by applying it to enrollment. Applicable Windows devices have their enrollment credentials stored in the device hardware, in this case Trusted Platform Module (TPM) 2.0, and Intune can then attest to this storage—meaning that the device enrolled securely. Successful devices show as Completed in the report. Devices that are Not Started or Failed can be retried using the new Device Attest at the top of the report. This will be available in public preview by the end of June.

Screenshot of the preview of the device attestation status report in the Intune admin center listing the name, ID, and primary UPN of a device that failed device attestation.

Stay up to date on the release of this capability to the public Microsoft 365 roadmap.

More granular endpoint security access controls

Role-based access control (RBAC) enhances organizations’ ability to configure access to specific workloads while maintaining a robust security posture. Our customers asked for even more granular controls to help scope security work across geographic areas, business units, or different teams to only relevant information and features. In this latest release, we are adding specific permission sets to enable more flexibility in creating custom roles for:

Endpoint detection and response
Application control
Attack surface reduction

We plan to have new permission sets for all endpoint security workloads in the future.

We know that many of our customers use a custom role with Security baselines permission to manage security workloads, so we are automatically adding the new permissions to this role. This way, no permissions will be lost for existing users. For new custom roles that are granted Security baselines permission, these will not include the new permissions by default but rather only those without specific permission sets.

This update also applies to customers using the Microsoft Defender console to manage security policies, and it is available in GCC, GCC High, and DoD environments. Read more about granular RBAC permissions.

So much of what we do is a direct result of customer feedback. Please join our community, visit the Microsoft Feedback portal, or leave a comment on this post. We value all your input, so please share it, especially after working with these exciting new capabilities.

Stay up to date! Bookmark the Microsoft Intune Blog and follow us @MSIntune on X and on LinkedIn to continue the conversation.

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I am trying to connect Copilot for service to my teams. Copilot for Service is showing a disconnect sign when I have a meeting with clients. Does anyone know what the issue is. 

​I am trying to connect Copilot for service to my teams. Copilot for Service is showing a disconnect sign when I have a meeting with clients. Does anyone know what the issue is.     Read More

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Hi all,

1.This is my table from that I have to select that not null values of ID1 and ID3, also select NULL value of ID2 and ID4 not null value.

2. If ID1,ID3 having both NULL and NOT NULL values means  show all NOT NULL values.

3. ID2 having NULL values means show that NULL value of ID2.

4.ID 4 has NOT NULL value  means show that ID4  NOT NULL value .

if the records have similar columns then take not null values, no similar take whether null or not null

 output should be like this :

can anyone please help me with this, Thanks in advance!!

​Hi all,1.This is my table from that I have to select that not null values of ID1 and ID3, also select NULL value of ID2 and ID4 not null value.2. If ID1,ID3 having both NULL and NOT NULL values means  show all NOT NULL values.3. ID2 having NULL values means show that NULL value of ID2.4.ID 4 has NOT NULL value  means show that ID4  NOT NULL value .if the records have similar columns then take not null values, no similar take whether null or not null output should be like this : can anyone please help me with this, Thanks in advance!!   Read More

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